The present invention relates to an automatic parcel volume capture system and a volume capture method using parcel image recognition. In particular, the automatic parcel volume capture system and the volume capture method utilize image recognition techniques to extract feature of a parcel and measure accurate volume of it.
Conventional volume measurement systems have employed cameras or laser devices. Light curtain technology and PILAR (Parallel Infrared Laser Rangefinder) use laser devices. On the other hand, a method using two still images, one side view and the other front view, utilizes cameras.
The method with laser devices is usually used for recognizing moving objects and this method requires large cost for equipments. In the method with cameras, objects to be recognized are very sensitive with lighting variation, which actually affects image processing and feature extraction for parcel image recognition. Therefore, this method with camera is not appropriate for parcel image recognition because the environment processing parcels is not good enough. And if shape of the parcel is not rectangular in the method with two images, efficiency of recognition is deteriorated.
Conventional volume measurement systems have employed edge detection techniques such as Sobel operator to recognize edges of input images and the edge detection techniques are basically based upon difference between adjacent pixels in terms of brightness. However, if variation of brightness in an image is big, the conventional volume measurement systems are not able to detect edges efficiently and therefore error of volume measurement increases.
An automatic parcel volume capture system and an automatic parcel volume capture method are provided. An automatic parcel volume capture system in accordance with an embodiment of the present invention includes stereo image input means, image processing means, feature extraction means, and volume measurement means. The stereo image input means captures images of an object from at least two different angles. The image processing means performs signal-processing on the images captured by the stereo image input means and extracts region of object in the images. The feature extraction means extracts lines and cross points of the lines from results of the image processing means. The volume measurement means generates three dimensional model on the basis of the extracted images and measures volume of the object.
Preferably, the automatic parcel volume capture system further includes volume storage means for storing volume of the object measured by the volume measurement means.
Preferably, the stereo image input means includes image capturing means and image preprocessing means. The image capturing means captures images of the object from at least two different angles. The image preprocessing means averages the captured images and removes noises.
Preferably, the image capturing means is a charge coupled device (CCD) camera.
Preferably, the image processing means includes edge detecting means and region extracting means. The edge detecting means detects all edges in the captured images. The region extracting means extracts object region by comparing background image with object image in reference to the detected edges.
Preferably, the feature extraction means includes line extracting means and feature point extracting means. The line extracting means extracts lines of an object from result of the image processing means. The feature point extracting means extracts crossing points of an object by finding intersection points of the extracted lines.
Preferably, the volume measurement means includes matched junction capturing means, three dimensional model generating means, and volume calculating means.
The matched junction capturing means matches same crossing points among crossing points captured from the image. The three dimensional model generating means generates three dimensional model of an object on the basis of the matched junction captured by the matched junction capturing means. The volume calculating means calculates volume of the object on the basis of the three dimensional model.
Preferably, the volume measurement means further includes error minimizing means for compensating error of the three dimensional model generated by the three dimensional model generating means.
An automatic parcel volume capture method in accordance with an embodiment of the present invention includes stereo image input step, image processing step, feature extraction step, and volume measurement step. The stereo image input step captures images of an object from at least two different angles. The image processing step performs signal-processing on the images captured at the stereo image input step and extracts region of object in the images. The feature extraction step extracts lines and crossing points of the lines from results of the image processing step. The volume measurement step generates three dimensional model on the basis of the extracted images and measures volume of the object.
Preferably, the volume storage step stores volume of the object measured by the volume measurement step.
Preferably, the stereo image input step includes image capturing step and image preprocessing step. The image capturing step captures images of the object from at least two different angles. The image preprocessing step averages the captured images and removes noises.
Preferably, the image preprocessing step includes following steps. First step covers a Wxc3x97W window around a current pixel x in Nxc3x97N input image. Second step calculates local average and variation regarding all pixels in the window. Third step applies the average and the variation to the following equation 1 for MDIM (Mean difference Dynamic Image Model) and applies the average and the variation to the following equation 2 for NDIM (Normalized Dynamic Image Model). Fourth step repeats the first step to third step with increasing the current pixel x up to Nxc3x97N sequentially.
Im2(x+xcex4x)=xcex1(x)xc2x7Im1(x), where Imi(x)=Ii(x)xe2x88x92mi(x)xe2x80x83xe2x80x83[Equation 1]
Ims2(x+xcex4x)=Ims1(x), where Imsi(x)=(Ii(x)xe2x88x92mi(x))/S1(x)xe2x80x83xe2x80x83[Equation 2]
x: position of the current pixel
Ii(x): brightness of x
xcex1(x): local brightness changing component
S1(x): square value of the local variation
mi(x): local average of x
Imi(x): difference between brightness of x Ii(x) and local average of x mi(x)
Imsi(x): Imi(x) divided by Si(x)
Preferably, the image processing step includes edge detecting step and region extracting step. The edge detecting step detects all edges in the captured images. The region extracting step extracts object region by comparing background image with object image in reference to the detected edges.
Preferably, the edge detecting step includes following steps. First step samples an Nxc3x97N image, calculates average and variation regarding the sampled image, and obtains statistical feature of the image. Second step extracts candidate edge pixels among all pixels in the image, brightness of the candidate edge pixels being significantly different from brightness of adjacent pixels. Third step connects candidate edge pixels extracted at the second step. Fourth step stores the candidate edge pixels as final edge pixels if length of the connected pixels is longer than threshold length or stores the candidate edge pixels as non-edge pixels if length of the connected pixels is shorter than threshold length.
Preferably, the second step detects maximum value and minimum value among differences between brightness of current pixel and brightness of eight adjacent pixels, classifies the current pixel as into a non-edge pixel if the maximum value and the minimum value are smaller than threshold value, and classifies the current pixel as into an edge pixel if the maximum value and the minimum value are bigger than threshold value, the threshold value being determined by statistical feature of the image.
Preferably, the third step determines magnitude and direction of an edge by applying Sobel operator to the candidate edge pixels, classifies the edge pixel whose magnitude and direction are determined into a non-edge pixel if magnitude of the edge pixel is smaller than magnitude of other candidate edge pixels, and connects remaining candidate edge pixels with adjacent candidate edge pixels.
Preferably, the feature extraction step includes line extracting step and feature point extracting means. The line extracting step extracts lines of an object from result of the image processing step. The feature point extracting step extracts crossing points of an object by finding intersection points of the extracted lines.
Preferably, the volume measurement step includes matched junction capturing step, three dimensional model generating step, and volume calculating step. The matched junction capturing step matches same crossing points among crossing points captured from the image. The three dimensional model generating step generates three dimensional model of an object on the basis of the matched junction captured at the matched junction capturing step. The volume calculating step calculates volume of the object on the basis of the three dimensional model.
Preferably, the volume measurement step further includes error minimizing step for compensating error of the three dimensional model generated by the three dimensional model generating step.
Preferably, the matched junction capturing step captures matched junction by utilizing crossing points of the object captured in the image and epipolar geometry.
An automatic parcel volume capture method implemented in a computer system is provided. An automatic parcel volume capture method implemented in a computer system in accordance with an embodiment of the present invention includes stereo image input step, image processing step, feature extraction step, and volume measurement step. The stereo image input step captures images of an object from at least two different angles. The image processing step performs signal-processing on the images captured at the stereo image input step and extracts region of object in the images. The feature extraction step extracts lines and crossing points of the lines from results of the image processing step. The volume measurement step generates three dimensional model on the basis of the extracted images and measures volume of the object.